18 research outputs found

    Distinct hippocampal engrams control extinction and relapse of fear memory

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    Learned fear often relapses after extinction, suggesting that extinction training generates a new memory that coexists with the original fear memory; however, the mechanisms governing the expression of competing fear and extinction memories remain unclear. We used activity-dependent neural tagging to investigate representations of fear and extinction memories in the dentate gyrus. We demonstrate that extinction training suppresses reactivation of contextual fear engram cells while activating a second ensemble, a putative extinction engram. Optogenetic inhibition of neurons that were active during extinction training increased fear after extinction training, whereas silencing neurons that were active during fear training reduced spontaneous recovery of fear. Optogenetic stimulation of fear acquisition neurons increased fear, while stimulation of extinction neurons suppressed fear and prevented spontaneous recovery. Our results indicate that the hippocampus generates a fear extinction representation and that interactions between hippocampal fear and extinction representations govern the suppression and relapse of fear after extinction.We thank J. Dunsmoor for comments on the manuscript. A.F.L. was supported by NIH F31 MH111243 and NIH T32 MH106454. S.L.S. was supported by PD/BD/128076/2016 from the Portuguese Foundation for Science and Technology. Research supported by NIH DP5 OD017908 and New York Stem Cell Science (NYSTEM) C-029157 to C.A.D., NIH R01 MH102595 and NIH R21 EY026446 to M.R.

    Excitation of Diverse Classes of Cholecystokinin Interneurons in the Basal Amygdala Facilitates Fear Extinction

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    There is growing evidence that interneurons (INs) orchestrate neural activity and plasticity in corticoamygdala circuits to regulate fear behaviors. However, defining the precise role of cholecystokinin-expressing INs (CCK INs) remains elusive due to the technical challenge of parsing this population from CCK-expressing principal neurons (CCK PNs). Here, we used an intersectional genetic strategy in CCK-Cre;Dlx5/6-Flpe double-transgenic mice to study the anatomical, molecular and electrophysiological properties of CCK INs in the basal amygdala (BA) and optogenetically manipulate these cells during fear extinction. Electrophysiological recordings confirmed that this strategy targeted GABAergic cells and that a significant proportion expressed functional cannabinoid CB1 receptors; a defining characteristic of CCK-expressing basket cells. However, immunostaining showed that subsets of the genetically-targeted cells expressed either neuropeptide Y (NPY; 29%) or parvalbumin (PV; 17%), but not somatostatin (SOM) or Ca2+/calmodulin-dependent protein kinase II (CaMKII)-α. Further morphological and electrophysiological analyses showed that four IN types could be identified among the EYFP-expressing cells: CCK/cannabinoid receptor type 1 (CB1R)-expressing basket cells, neurogliaform cells, PV+ basket cells, and PV+ axo-axonic cells. At the behavioral level, in vivo optogenetic photostimulation of the targeted population during extinction acquisition led to reduced freezing on a light-free extinction retrieval test, indicating extinction memory facilitation; whereas photosilencing was without effect. Conversely, non-selective (i.e., inclusive of INs and PNs) photostimulation or photosilencing of CCK-targeted cells, using CCK-Cre single-transgenic mice, impaired extinction. These data reveal an unexpectedly high degree of phenotypic complexity in a unique population of extinction-modulating BA INs

    Clinical outcomes and response to treatment of patients receiving topical treatments for pyoderma gangrenosum: a prospective cohort study

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    Background: pyoderma gangrenosum (PG) is an uncommon dermatosis with a limited evidence base for treatment. Objective: to estimate the effectiveness of topical therapies in the treatment of PG. Methods: prospective cohort study of UK secondary care patients with a clinical diagnosis of PG suitable for topical treatment (recruited July 2009 to June 2012). Participants received topical therapy following normal clinical practice (mainly Class I-III topical corticosteroids, tacrolimus 0.03% or 0.1%). Primary outcome: speed of healing at 6 weeks. Secondary outcomes: proportion healed by 6 months; time to healing; global assessment; inflammation; pain; quality-of-life; treatment failure and recurrence. Results: Sixty-six patients (22 to 85 years) were enrolled. Clobetasol propionate 0.05% was the most commonly prescribed therapy. Overall, 28/66 (43.8%) of ulcers healed by 6 months. Median time-to-healing was 145 days (95% CI: 96 days, ∞). Initial ulcer size was a significant predictor of time-to-healing (hazard ratio 0.94 (0.88;80 1.00); p = 0.043). Four patients (15%) had a recurrence. Limitations: No randomised comparator Conclusion: Topical therapy is potentially an effective first-line treatment for PG that avoids possible side effects associated with systemic therapy. It remains unclear whether more severe disease will respond adequately to topical therapy alone

    Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries

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    Funding Information: M.B.A. holds a Tier 2 Canada Research Chair in the Developmental Origins of Chronic Disease at the University of Manitoba and is a Fellow in the Canadian Institutes for Advanced Research (CIFAR) Humans and the Microbiome Program. Her effort on this project was partly supported by HDR UK and ICODA. K.K.C.M. declares support from The Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, and Hong Kong Research Grants Council Collaborative Research Fund Coronavirus Disease (COVID-19) and Novel Infectious Disease Research Exercise (Ref: C7154-20G) and grants from C W Maplethorpe Fellowship, National Institute of Health Research UK, European Commission Framework Horizon 2020 and has consulted for IQVIA Ltd. A.S. is supported by ICODA and HDR UK, and has received a research grant from HDR UK to the BREATHE Hub. He participates on the Scottish and UK Government COVID-19 Advisory Committees, unremunerated. S.J.S. is supported by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z) and HDR UK, and has received personal fees from Hologic and Natera outside the submitted work. D.B. is supported by a National Health and Medical Research Council (Australia) Investigator Grant (GTN1175744). I.C.K.W. declares support from The Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, and Hong Kong Research Grants Council Collaborative Research Fund Coronavirus Disease (COVID-19) and Novel Infectious Disease Research Exercise (Ref: C7154-20G), and grants from Hong Kong Research Grant Council, National Institute of Health Research UK, and European Commission Framework Horizon 2020. H.Z. is supported by a UNSW Scientia Program Award and reports grants from European Commission Framework Horizon 2020, Icelandic Centre for Research, and Australia’s National Health and Medical Research Council. H.Z. was an employee of the UNSW Centre for Big Data Research in Health, which received funding from AbbVie Australia to conduct research, unrelated to the current study. I.I.A.A., C.D.A., K.A., A.I.A., L.C., S.S., G.E.-G., O.W.G., L. Huicho, S.H., A.K., K.L., V.N., I.P., N.R.R., T.R., T.A.H.R., V.L.S., E.M.S., L.T., R.W. and H.Z. received funding from HDRUK (grant #2020.106) to support data collection for the iPOP study. K.H., R.B., S.O.E., A.R.-P. and J.H. receive salary from ICODA. M.B. received trainee funding from HDRUK (grant #2020.106). J.E.M. received trainee funding from HDRUK (grant #2020.109). Other relevant funding awarded to authors to conduct research for iPOP include: M.G. received funding from THL, Finnish Institute for Health and Welfare to support data collection. K.D. received funding from EDCTP RIA2019 and HDRUK (grant #2020.106) to support data collection. R.B. received funding from Alzheimer’s Disease Data Initiative and ICODA for the development of federated analysis. A.D.M. received funding from HDR UK who receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust; and Administrative Data Research UK, which is funded by the Economic and Social Research Council (grant ES/S007393/1). N.A. received funding from the National Institutes of Health (R35GM138353). O.S received funding from NordForsk (grant #105545). The remaining authors declare no competing interests. Funding Information: Funding and in-kind support: This work was supported by the International COVID-19 Data Alliance (ICODA), an initiative funded by the Bill and Melinda Gates Foundation and Minderoo as part of the COVID-19 Therapeutics Accelerator and convened by Health Data Research (HDR) UK, in addition to support from the HDR UK BREATHE Hub. Several ICODA partners contributed to the study, including: Cytel (statistical support), the Odd Group (data visualization) and Aridhia Informatics (development of federated analysis using a standardized protocol ([Common API] https://github.com/federated-data-sharing/ ) to be used in future work). Additional contributors: We acknowledge the important contributions from the following individuals: A. C. Hennemann and D. Suguitani (patient partners from Prematuridade: Brazilian Parents of Preemies’ Association, Porto Alegre, Brazil); N. Postlethwaite (implementation of processes supporting the trustworthy collection, governance and analysis of data from ICODA, HDR UK, London, UK); A. S. Babatunde (led data acquisition from University of Uyo Teaching Hospital, Uyo, Nigeria); N. Silva (data quality, revision and visualization assessment from Methods, Analytics and Technology for Health (M.A.T.H) Consortium, Belo Horizonte, Brazil); J. Söderling (data management from the Karolinska Institutet, Stockholm, Sweden). We also acknowledge the following individuals who assisted with data collection efforts: R. Goemaes (Study Centre for Perinatal Epidemiology (SPE), Brussels, Belgium); C. Leroy (Le Centre d'ÉpidĂ©miologie PĂ©rinatale (CEpiP), Brussels, Belgium); J. Gamba and K. Ronald (St. Francis Nsambya Hospital, Kampala, Uganda); M. Heidarzadeh (Tabriz Medical University, Tabriz, Iran); M. J. Ojeda (Pontificia Universidad CatĂłlica de Chile, Santiago, Chile); S. Nangia (Lady Hardinge Medical College, New Delhi, India); C. Nelson, S. Metcalfe and W. Luo (Maternal Infant Health Section of the Public Health Agency of Canada, Ottawa, Canada); K. Sitcov (Foundation for Health Care Quality, Seattle, United States); A. Valek (Semmelweis University, Budapest, Hungary); M. R. Yanlin Liu (Mater Data and Analytics, Brisbane, Australia). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Funding Information: Funding and in-kind support: This work was supported by the International COVID-19 Data Alliance (ICODA), an initiative funded by the Bill and Melinda Gates Foundation and Minderoo as part of the COVID-19 Therapeutics Accelerator and convened by Health Data Research (HDR) UK, in addition to support from the HDR UK BREATHE Hub. Several ICODA partners contributed to the study, including: Cytel (statistical support), the Odd Group (data visualization) and Aridhia Informatics (development of federated analysis using a standardized protocol ([Common API] https://github.com/federated-data-sharing/) to be used in future work). Additional contributors: We acknowledge the important contributions from the following individuals: A. C. Hennemann and D. Suguitani (patient partners from Prematuridade: Brazilian Parents of Preemies’ Association, Porto Alegre, Brazil); N. Postlethwaite (implementation of processes supporting the trustworthy collection, governance and analysis of data from ICODA, HDR UK, London, UK); A. S. Babatunde (led data acquisition from University of Uyo Teaching Hospital, Uyo, Nigeria); N. Silva (data quality, revision and visualization assessment from Methods, Analytics and Technology for Health (M.A.T.H) Consortium, Belo Horizonte, Brazil); J. Söderling (data management from the Karolinska Institutet, Stockholm, Sweden). We also acknowledge the following individuals who assisted with data collection efforts: R. Goemaes (Study Centre for Perinatal Epidemiology (SPE), Brussels, Belgium); C. Leroy (Le Centre d'ÉpidĂ©miologie PĂ©rinatale (CEpiP), Brussels, Belgium); J. Gamba and K. Ronald (St. Francis Nsambya Hospital, Kampala, Uganda); M. Heidarzadeh (Tabriz Medical University, Tabriz, Iran); M. J. Ojeda (Pontificia Universidad CatĂłlica de Chile, Santiago, Chile); S. Nangia (Lady Hardinge Medical College, New Delhi, India); C. Nelson, S. Metcalfe and W. Luo (Maternal Infant Health Section of the Public Health Agency of Canada, Ottawa, Canada); K. Sitcov (Foundation for Health Care Quality, Seattle, United States); A. Valek (Semmelweis University, Budapest, Hungary); M. R. Yanlin Liu (Mater Data and Analytics, Brisbane, Australia). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Publisher Copyright: © 2023, The Author(s).Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from −90% to +30%, were reported in many countries following early COVID-19 pandemic response measures (‘lockdowns’). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95–0.98, P value <0.0001), second (0.96, 0.92–0.99, 0.03) and third (0.97, 0.94–1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96–1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88–1.14, 0.98), third (0.99, 0.88–1.12, 0.89) and fourth (1.01, 0.87–1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02–1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03–1.15, 0.002), third (1.10, 1.03–1.17, 0.003) and fourth (1.12, 1.05–1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways.Peer reviewe

    Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries.

    Get PDF
    Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from -90% to +30%, were reported in many countries following early COVID-19 pandemic response measures ('lockdowns'). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95-0.98, P value <0.0001), second (0.96, 0.92-0.99, 0.03) and third (0.97, 0.94-1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96-1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88-1.14, 0.98), third (0.99, 0.88-1.12, 0.89) and fourth (1.01, 0.87-1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02-1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03-1.15, 0.002), third (1.10, 1.03-1.17, 0.003) and fourth (1.12, 1.05-1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways

    Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries.

    Get PDF
    Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from -90% to +30%, were reported in many countries following early COVID-19 pandemic response measures ('lockdowns'). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95-0.98, P value <0.0001), second (0.96, 0.92-0.99, 0.03) and third (0.97, 0.94-1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96-1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88-1.14, 0.98), third (0.99, 0.88-1.12, 0.89) and fourth (1.01, 0.87-1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02-1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03-1.15, 0.002), third (1.10, 1.03-1.17, 0.003) and fourth (1.12, 1.05-1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways

    Dorsolateral Striatum Engagement Interferes with Early Discrimination Learning

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    Summary: In current models, learning the relationship between environmental stimuli and the outcomes of actions involves both stimulus-driven and goal-directed systems, mediated in part by the DLS and DMS, respectively. However, though these models emphasize the importance of the DLS in governing actions after extensive experience has accumulated, there is growing evidence of DLS engagement from the onset of training. Here, we used in vivo photosilencing to reveal that DLS recruitment interferes with early touchscreen discrimination learning. We also show that the direct output pathway of the DLS is preferentially recruited and causally involved in early learning and find that silencing the normal contribution of the DLS produces plasticity-related alterations in a PL-DMS circuit. These data provide further evidence suggesting that the DLS is recruited in the construction of stimulus-elicited actions that ultimately automate behavior and liberate cognitive resources for other demands, but with a cost to performance at the outset of learning. : What is the contribution of the DLS in early discrimination learning? Bergstrom et al. show using in vivo optogenetics, fluorescence in situ hybridization, and brain-wide activity mapping that silencing the DLS facilitates early discrimination learning, drives activity in a parallel PL-DMS circuit, and preferentially recruits the DLS “direct” output pathway. Keywords: striatum, reward, goal-directed, habit, optogenetics, plasticity, cognition, Ar
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